Linked Open Vocabulary Recommendation Based on Ranking and Linked Open Data

  • Ioannis StavrakantonakisEmail author
  • Anna Fensel
  • Dieter Fensel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9544)


The vocabulary space of the Semantic Web includes more than 500 vocabularies according to the Linked Open Vocabularies (LOV) initiative that maintains the directory list and provides search functionality on top of the curated data. Domain experts and researchers have populated it to facilitate the interpretation and exchange of information in the Web of Data. The abundance of vocabularies and terms available in the LOV space, on one hand aims to cover the major knowledge management needs, but on the other hand it could be cumbersome for a non-expert or even a vocabulary expert to find the correct way through the collection. To address this problem, we present an approach that helps to identify the most appropriate set of LOV vocabulary terms for a given Web content context by leveraging the existing dynamics within the LOV graph and the usage patterns in the LOD cloud. The paper describes the framework architecture that enables the discovery of vocabularies; it focuses on the corresponding metrics and algorithm, and discusses the outcomes of the applied experiments.


Natural Language Processing Ranking Score Semantic Annotation Candidate Term Outgoing Link 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by the EU projects BYTE, ENTROPY, EUTravel, FWF project OntoHealth, as well as FFG projects OpenFridge and TourPack.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ioannis Stavrakantonakis
    • 1
    Email author
  • Anna Fensel
    • 1
  • Dieter Fensel
    • 1
  1. 1.University of Innsbruck, STI InnsbruckInnsbruckAustria

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